Abstract
Automated chromosome classification is a vital task in cytogenetics and has been a common pattern recognition problem. Numerous attempts were made in the past decade years to characterize chromosomes for the intention of medical and cancer cytogenetics research. This paper proposes a graphic geometry-based approach for medial axis extraction and Competitive Extreme Learning Machine Teams (CELMT) with further correction method for chromosome classification. The initial two medial axis points are determined firstly according to the length of the intercept line in different directions, and then the complete medial axis for feature extraction is drawn based on the initial two points. After that, a base classifier ELMi, j is trained to differentiate a pair class chromosome (i, j), a total number of 276 classifiers are trained. Each base classifier will give a label and the final label will be determined by majority voting and further correction rules. Based on the experiment results, the method proposed in this paper can precisely extract the medial axis and extract the features to recognize the chromosome, the classification accuracy by using CELMT can achieve an average value of 96.23% and the running time is much shorter than the other classification algorithms.
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This work was supported by the National Natural Science Foundation of China (61922072, 61876169, 61673404).
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Wang, J., Zhao, C., Liang, J., Yue, C., Ren, X., Bai, K. (2020). Chromosome Medial Axis Extraction Method Based on Graphic Geometry and Competitive Extreme Learning Machines Teams (CELMT) Classifier for Chromosome Classification. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_46
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